This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

Objective

To investigate the predictive value of different biomarkers for the incidence of type
2 diabetes mellitus (T2DM) in subjects with metabolic syndrome.

Methods

A prospective study of 525 non-diabetic, middle-aged Lithuanian men and women with
metabolic syndrome but without overt atherosclerotic diseases during a follow-up period
of two to four years. We used logistic regression to develop predictive models for
incident cases and to investigate the association between various markers and the
onset of T2DM.

Results

Fasting plasma glucose (FPG), body mass index (BMI), and glycosylated haemoglobin
can be used to predict diabetes onset with a high level of accuracy and each was shown
to have a cumulative predictive value. The estimated area under the receiver-operating
characteristic curve (AUC) for this combination was 0.92. The oral glucose tolerance
test (OGTT) did not show cumulative predictive value. Additionally, progression to
diabetes was associated with high values of aortic pulse-wave velocity (aPWV).

Conclusion

T2DM onset in middle-aged metabolic syndrome subjects can be predicted with remarkable
accuracy using the combination of FPG, BMI, and HbA1c, and is related to elevated aPWV measurements.

Keywords:

Background

Metabolic syndrome (MetS) is a complex disorder defined by a cluster of interconnected
factors that increase the risk of cardiovascular (CV) atherosclerotic diseases and
type 2 diabetes mellitus (T2DM). The presence of MetS as a risk factor for T2DM has
been examined in numerous population-based studies [1-5]. The meta-analysis of prospective studies shows MetS to be associated with an approximately
five times higher risk for incident T2DM in many different populations, regardless
of how the MetS is defined [6]. Impaired fasting glucose (IFG) and impaired glucose tolerance (IGT) are shown to
be strong predictors of T2DM in many studies [7-9]. Other components of MetS, particularly waist circumference (WC), body mass index
(BMI), and triglycerides were shown to be associated with incidence of T2DM in cohorts
composed of subjects with high post-prandial glucose [10] and in the general population [11]. A recent study in patients with manifest atherosclerosis revealed that the presence
of ≥ 3 metabolic risk factors or the presence of a high waist circumference alone
are associated with increased risk for developing T2DM [12]. The combined presence of ≥ 3 metabolic risk factors and high waist circumference
is associated with a 10-fold increased risk of future T2DM [12].

To date, only limited information is available on the predictors of T2DM in the group
of patients that are already diagnosed with MetS but no overt atherosclerotic disease.
While the majority of available studies report associations between the incidence
of T2DM and the presence of MetS or other risk factors, the analysis of the predictive
and cumulative value of these factors is lacking. The aim of our study was to investigate
the predictive value of different clinical markers, including the ones described above,
for T2DM onset in subjects with MetS before the manifestation of atherosclerotic disease.

Methods

Subject recruitment

All patients included in our study were recruited between 2007 and 2011 from the Lithuanian
High Cardiovascular Risk (LitHiR) primary prevention programme [13]. This long-term programme has focused on employable-aged women (aged 50–65) and men
(aged 40–55) without overt cardiovascular disease. Cardiovascular disease was defined
as angina pectoris, known coronary stenosis, myocardial infarction, coronary artery bypass grafting, percutaneous coronary intervention, transient ischemic
attack or stroke, and peripheral artery disease. As part of the programme, a two-level
approach involving primary healthcare institutions (PHCI) and specialized cardiovascular
prevention units (CVPU) was applied. Five secondary-level institutions having CVPU
participated in the LitHiR programme across Lithuania, including the Vilnius University
Hospital Santariškių Klinikos. Participants of the first level of the programme were
recruited in three ways. The first group consisted of people registered in PHCI and
invited by general practitioners to participate in the programme. The second group
consisted of people who visited PHCIs for reasons other than cardiovascular problems.
The third group included people who found out about the programme via local mass media.
All participants had to match the programme criteria. After cardiovascular risk evaluation
at the PHCI level, subjects for whom high cardiovascular risk was established were
sent for additional examination and treatment plans in the CVPUs (secondary level).
High cardiovascular risk was defined as having one or more of the following conditions:
1) a Systematic Coronary Risk Evaluation (SCORE) [14] risk assessment of over 11, 2) diabetes, 3) metabolic syndrome, 4) positive family
history of cardiovascular disease and/or 4) severe dyslipidemia.

The number of PHCIs taking part in this program was 385/420, which comprise 91.6%
of all PHCI in Lithuania. From 2006 to 2010, 266,391 patients were examined overall.
Out of those patients, our cohort includes 2891 [1072 (37%) men and 1819 (63%) women]
patients who were diagnosed with MetS and referred to the CVPU at the Vilnius University
Hospital Santariškių Klinikos for additional assessment, risk stratification, and
setting up of a prevention plan.

We carried out follow-up calls between January 2011 and August 2011 for 650 out of
the 2891 subjects with MetS initially referred to the CPVU in Vilnius University Hospital
Santariškių Klinikos. These follow-up calls were made with preference to subjects
who were examined earlier in the programme. After we excluded four subjects whose
follow-up periods were less than two years, the median of the follow-up period was
3.3 years. We also excluded 117 participants who already had diabetes at the baseline
examination and 4 participants with missing information on their diabetic status.
As a result, the final study cohort consisted of 525 individuals, with 187 (36%) men
and 338 (64%) women.

The study was approved by the Local Ethics Committee of the Vilnius University Hospital
Santariškių Klinikos.

Diagnosis of MetS

We diagnosed patients as having MetS if they met three or more of the revised National
Cholesterol Education Program Adult Treatment Panel III (NCEP ATPIII) criteria [15,16]:

Baseline examinations

All participants in our study underwent a baseline examination, which included gathering
information on their medical history, physical examination, risk profile and lifestyle
assessment, evaluation of cardiovascular (CV) family history, 12-lead electrocardiogram
(ECG), laboratory blood tests, and non-invasive assessment of arterial markers of
subclinical atherosclerosis. Weight, height, and waist circumference were measured
with the subject wearing light clothing and without shoes. BMI was calculated as weight
in kilograms divided by the square of height in meters. Blood pressure was measured
after the patient rested at least five minutes, using an oscillometric semiautomatic
device (Schiller Argus VCM) with a standard bladder (12–13 cm long and 35 cm wide),
validated according to standardized mercury sphygmomanometer. We took at least one
measurement on each arm and additional measurements if the first two were significantly
different. The higher value was taken as the reference one and the average of the
two highest values, if measured more than twice. Assessment of arterial stiffness
was carried out by applanation tonometry (Sphygmocor v.7.01, AtCor Medical).

Information about smoking and drug use was collected by a questionnaire. Current smoking
was recorded if the subject smoked at least one cigarette a day. Positive CV family
history was recorded if first-degree relatives of the patient had any CV events at
a young age (men ≤ 45 years, women ≤ 55 years old).

We measured the association between each variable and the development of T2DM by calculating
gender-adjusted odds ratios (ORs). We initially included the gender variable in any
set of predictors tested. We investigated the dependency between variables and their
cumulative contribution to the prediction based on their combined logistic regression
model. P values are based on two-sided tests with a cutoff for statistical significance
of 0.05. To address the inherent problem of multiple hypotheses testing, we applied
the Bonferroni correction, multiplying the P value by the number of independent tests.

We performed all tests on complete data; that is, excluding those patients with data
missing for the relevant variables. We used Little’s [28] missing completely at random (MCAR) test to identify systematic differences between
the missing values and the observed values. A significant P value in Little’s MCAR
test, indicating the existence of such systematic differences, means that it is plausible
that data are missing at random (MAR), but not completely at random (MCAR). In these
cases, since restricting analyses to complete cases can introduce bias, we validated
the results using multiple imputation [29,30]. We used the fully conditional specification [31] imputation method, as implemented in SPSS MULTIPLE IMPUTATION command, to make 20
complete datasets. We then combined (pooled) multiple analyses’ results using Rubin’s
Rules [30,32].

In a separate analysis, we considered the tested variables using a stepwise algorithm
that automatically selected variables for a multivariate logistic regression model.
This method used the Bayesian Information Criterion (BIC), which assesses model fit
based on a log-likelihood function [33]. The model with the lowest value of BIC is the one preferred. We took a “forward”
approach, starting with a model initialized with the gender variable, adding at each
step one variable that maximally reduced the BIC statistic and terminated when the
BIC statistic stopped decreasing. We estimated the accuracy of the predictive models
using leave-one-out cross-validation; that is, each subject in its turn was used as
a validation set, while the remaining subjects were used to generate the model. We
assessed the predictive discrimination of the model using the receiver-operating characteristic
(ROC) curve of the scores of all subjects by plotting the sensitivity against the
corresponding false positive rate. We used the area under the ROC curve, calculated
by the trapezoidal rule, to measure how well a model predicts the development of T2DM.
The model generation involved a preliminary step of data imputation for missing values
using mean values. We also used an alternative analyses using K-nearest-neighbors
data imputation, which yielded similar results; only the mean imputation results are
presented.

All statistical and modeling analysis was done using MATLAB 7.13 (R2011b) and SPSS
Statistics 19.0.0.

Results

We observed data from 187 men and 338 women with mean ±SD ages at a baseline of 48±4
and 57±4 years for an average of 3.2 and 3.3 years, respectively. During the follow-up
period of 2 to 4 years, a total of 32 subjects progressed to diabetes: 16 (8.5%) of
the 187 men and 16 (5%) of the 338 women. Table 1 shows the baseline characteristics of the two groups: progressors and nonprogressors.

Missing values

One hundred (19%) of the subjects had missing OGTT glucose test values, and 120 (23%)
of the subjects had missing HbA1c values. Applying Little’s MCAR test on the entire set of variables had a significant
result (χ2(636)=589.6, P<0.001). Repeating Little’s MCAR test after the exclusion of these variables
led to non-significant results (χ2(180)=179.6, P>0.1). These results were expected as the examination protocol recommended
OGTT, HbA1c and fasting insulin tests in patients with higher FPG values. Since missing values
were not missing completely at random (MCAR), we validated our results in multiple
imputation analysis (see “Statistical Analysis” Section).

Baseline classification of subjects

At the baseline, 237 (45%) had NGT, 99 (19%) had impaired fasting glucose (IFG), and
67 (13%) had impaired glucose tolerance (IGT). Twenty two (4%) subjects had FPG ≥7
mmol/l, but were diagnosed as non-diabetic by an endocrinologist, based on additional
test results (including former fasting glucose test). One hundred (19%) of the subjects
were not classified mainly due to missing OGTT glucose test values. In the multiple
imputation analysis, most of the unclassified patients were in the NGT group, which
then increased to 60% (95% confidence interval [CI] 58-61%) of the patients. The IFG
and the IGT group contained 20% (CI 20-21%) and 16% (CI 14.5-17 %) of the patients,
respectively.

Impaired fasting glucose and impaired glucose tolerance

In this section, we report the results of multiple imputation analysis. Complete data
analysis had similar results (not shown). The association of T2DM onset with the IFG
and IGT groups was significant: the odds-ratio in the IFG group was 3.7 (CI 1.5-9.4
P= 0.006) and in the IGT group was 3.3 (CI 1.2-8.7, P=0.01). The odds ratios for T2DM
onset were higher when the underlying criteria for FPG [≥6.1, <7 mmol/l] and OGTT
glucose [≥6.1, <11 mmol/l] were combined: 11 (CI 3.2-38, P = 0.0001) in subjects satisfying
at least one criterion, and 7.9 (CI = 2.8-22.4, P = 0.0001) in subjects satisfying
both. The FPG criterion alone showed an even stronger association with T2DM onset:
odds-ratio 12.3 (CI 4.1-37.4, P<0.0001).

Identifying an effective set of predictors for T2DM

We found a combination of variables that effectively predicts T2DM using the following
iterative analysis. Iteratively, after adjusting for previously included variables,
we added to the set of predictors the strongest predictor for T2DM whose cumulative
effect was shown to be significant (Bonferonni corrected P < 0.05, odds-ratio test).
The iteration ended when no variable could be added. We report the results of multiple
imputation analysis. Complete data analysis yielded similar results (not shown). Table 2 presents the odds-ratio results for all variables after they were adjusted for gender.
In the first iteration, we identified 11 significant predictors (presented in decreasing
order of their association): FPG, BMI, Waist circumference, OGTT glucose, HbA1c, Quicki, MetS score, Weight, ISIMatsuda, OGTT insuline, HOMA-IR, and Fasting Insuline. In the second iteration, after adjusting
for FPG and gender, the BMI showed the most significant association. After the selection
of BMI (third iteration), only HbA1c remained a significant predictor. The final set included: gender, FPG, BMI and HbA1c. The selected variables: FPG, BMI and HbA1c, each showed a significant cumulative effect in the final model (FPG: P=0.000001;
BMI: 0.00001; HbA1c: P=0.0004).

Model selection and accuracy estimation

In a separate analysis, we tested a model selection algorithm for building a predictive
model for T2DM. This algorithm used a stepwise multivariate logistic regression with
the Bayesian Information Criterion (BIC) measurement as a goodness-of-fit. To account
for gender differences, the initial model contained the gender variable. Notably,
the FPG-BMI-HbA1c combination was consistently selected for all training sets. The overall estimated
accuracy of the model was remarkably high (AUC=0.91). Figure 1 exemplifies the predictive power of FPG, BMI, and HbA1c, as well as the improvement in the prediction for their combined, gender-adjusted
score, by plotting the ROC curves of the corresponding models.

Figure 1.Comparison of prediction models. ROC curves of four diabetes onset prediction models: FPG-model, BMI-model, HbA1c-model, and a FPG-BMI-HbA1c-model. All models were adjusted for the gender variable.

Comparison of BMI, waist circumference and weight

We tested the cumulative value of the obesity measures: BMI, waist circumference (WC),
and weight, with respect to one another by combining them all into one model and adjusting
for gender and FPG. Complete cases and multiple imputation analyses had similar results;
only the latter is reported. Under this model, BMI had the most significant cumulative
effect (P = 0.003, odds ratio test), compared to weight (P=0.03, odds ratio test),
and waist (P>0.1, odds ratio test).

Additionally, we compared the estimated accuracy of three prediction models, each
corresponding to one of the three of obesity measures, together with gender, FPG,
and HbA1c. All three models had high estimated accuracy (AUC: BMI: 0.91, Weight=0.9, Waist=0.92).
In summary, although BMI showed the strongest cumulative effect, all three obesity
measures exhibited comparable discrimination under a model that contains FPG, HbA1c, and gender.

OGTT glucose and FPG

We compared the cumulative values of OGTT glucose and FPG with respect to each other
by testing their combination. Complete cases and multiple imputation analysis were
in agreement; we report results for the latter. We tested the cumulative effect of
FPG and OGTT glucose in two settings: after adjustment to gender and after adjustment
to gender, BMI, and HbA1c. In both cases, FPG exhibited a very significant cumulative effect (P< 0.00001, odds-ratio
test). On the other hand, OGTT glucose showed a milder cumulative effect in the gender-adjusted
model (P=0.007, odds-ratio test), and no significant effect when BMI and HbA1c were added to the model (P> 0.1, odds-ratio test).

In an ROC analysis with cross validation, the FPG model exhibited better performance
than the OGTT glucose model (AUC: FPG=0.83, OGTT glucose = 0.71). The combined model
FPG-OGTT glucose did not show any improvement (AUC=0.83). This confirmed that in our
cohort, FPG is superior to OGTT glucose in predicting T2DM, and that OGTT glucose
shows no cumulative effect in a model that contains FPG.

Association of diabetes with arterial markers of cardiovascular risk

We used the following measures of arterial stiffness as surrogate markers for cardiovascular
risk (CVR) at baseline examination: aortic and radial pulse wave velocity (aPWV, rPWV)
adjusted to the mean arterial pressure (MAP), and aortic augmentation index adjusted
for a heart rate of 75 beats per minute (AIx@75). Complete case analysis showed that
Aix@75 and rPWV markers have no significant association with either progression to
diabetes or IGT/IFG pre-diabetes conditions. On the other hand, high aPWV values were
significantly associated with the IGT condition at baseline (P=0.01; odds ratio test)
and with progression to diabetes (P = 0.04; odds ratio test). Repeating the tests
with multiple imputations yielded no significant results. As aPWV seemed to be missing
completely at random (Little’s MCAR test), we repeated the multiple imputation analysis
after restricting the data to patients with non-missing aPWV values, retaining 480
(91%) of the patients. This time, the results of the multiple imputation analysis
matched the complete case analysis. Testing the association between aPWV and CRP yielded
no significant result (P>0.1, Pearson correlation test, complete case, and multiple
imputation analyses).

Discussion

In this study, the combination of FPG, BMI, and HbA1c was shown to be a powerful predictor for the development of T2DM in subjects with
MetS. FPG was shown to be superior to OGTT glucose in predicting T2DM, with OGTT glucose
showing no cumulative value to FPG. Our study is aligned with general population studies
showing that both IGT and IFG are similarly associated with an increased risk of diabetes,
and that risks are higher when IGT and IFG coexist [34]. IFG was more prevalent than IGT in our cohort, while the opposite trend is usually
observed in the general population [34]. The higher rate of IFG can be attributed to the fact that our study cohort consisted
of subjects with high metabolic risk, in whom higher values of FPG are expected. Our
findings of FPG being a stronger predictor than OGTT glucose and that OGTT exhibited
no cumulative value to FPG, are different from the reports of other studies [35,36]. This increased predication power of FPG can be explained by the high prevalence
of elevated FPG in our group. Similar to [37-39], which studied long-term prediction of T2DM risk in the general population, our results
do not support the need for performing a 2-h OGTT to pinpoint the possible candidates
for future diabetes in MetS subjects.

BMI and HbA1c were evaluated as predictors of diabetes in numerous studies. BMI is known to be
a major predictor for T2DM in the general population [35], as well as in the MetS population [11]. The T2DM risk was shown to increase exponentially with HbA1c in both genders [40]. In another large study, the model including both FPG and HbA1c was shown to be more effective for T2DM prediction than models including FPG alone
or HbA1c alone [41]. Recently, a study confirmed that HbA1c of ≥5.6% had an increased risk for progression to T2DM, independent of other confounding
factors [42]. This supports our finding on the cumulative effect of HbA1c, with respect to FPG and HbA1c.

Our investigation of four common insulin resistance/sensitivity indices yielded that
these are less predictive for T2DM than FPG and OGTT glucose, as previously indicated
by other studies whose cohorts were characterized by a high rate of IFG [37]. The association of these indices with progression to T2DM became insignificant after
adjusting for FPG. This is similar to another report [43], which tested the association of the HOMA-IR index with T2DM after adjustment for
BMI and familial history.

Our applanation tonometry results correspond with previous studies concerning the
association between aPWV and diabetes, and the lack of association between elevated
augmentation index and the presence of diabetes [44]. Similar to previous reports [45]

,

our study demonstrated that the association between increased aortic stiffness and
glucose metabolism abnormalities (IGT) is already found in pre-diabetic stages, and
that IGT is more strongly associated with cardiovascular risk than IFG. The increased
aPWV in our study cannot be explained by the elevation of CRP, and is predominantly
associated with elevated 2h-OGTT glucose measurements.

To the best of our knowledge, no previous study established a predictive model for
a new onset of diabetes in subjects with MetS. Since we focused on middle-aged metabolic-syndrome
subjects, a possible limitation of our study is that its results cannot be generalized
to subjects without MetS. Our study was also limited by the size of our dataset (525
subjects) and by the short duration of the follow-up period (2 to 4 years), resulting
in only 32 participants that developed diabetes during the follow-up period. The subsequent
unbalanced ratio between progressors and non-progressors, together with the relatively
small size of the dataset, led to higher uncertainty in assessing the level of the
risk estimate for considered variables. Another drawback of our study is the lack
of information on diabetes familial history, which was shown to be a strong predictor
for T2DM in the general population as well as in MetS subjects [11]. As 2h-OGTT glucose was found to be inferior to FPG in predicting T2DM in MetS subjects,
future studies should also consider 1h-OGTT glucose, which was found to be a stronger
predictor than 2h-OGTT glucose in several studies [37,46].

Conclusions

The main finding of our study suggests that simple measures, such as BMI, FPG, and
HbA1c can accurately predict the development of T2DM in subjects with MetS. Meta-analysis
of data from many population-based studies has shown that MetS, regardless of how
it is defined, is a significant predictor of incident diabetes in many different populations
[6]. Our study added to the current knowledge that for subjects who already have MetS,
no sophisticated tests are needed to accurately identify the risk of incident diabetes:
fasting plasma glucose is the strongest predictor with BMI and glycosylated haemoglobin
having cumulative value.

Competing interests

The authors declare that they have no competing interests.

Authors' contributions

MO researched data, wrote the manuscript, and is the guarantor of this work. NP researched
data and wrote the manuscript. TE researched the data and reviewed/edited the manuscript.
ZV wrote the manuscript. LR collected data and wrote the manuscript. JB, SS, and MK
collected data and reviewed the manuscript. RN and AL reviewed/edited the manuscript.
All authors read and approved the final manuscript.

Acknowledgements

The study was supported by the Vilnius University Hospital Santariškių Klinikos and
IBM Research.

Expert Panel on Detection Evaluation and Treatment of High Blood, Cholesterol in Adults: Executive Summary of The Third Report of The National Cholesterol Education Program
(NCEP) Expert Panel on Detection, Evaluation, And Treatment of High Blood Cholesterol
In Adults (Adult Treatment Panel III).

Alberti KG, Zimmet PZ: Definition, diagnosis and classification of diabetes mellitus and its complications.
Part 1: diagnosis and classification of diabetes mellitus provisional report of a
WHO consultation.